Satellite and aircraft remote sensing systems have been increasingly used to aid weather prediction, agricultural forecasting, resource exploration, land cover mapping, environmental monitoring, industrial plant monitoring, civil defense, and military surveillance. Hyperspectral imaging systems promise enhanced scene characterization relative to univariate and multispectral technologies for these applications. Using a hyperspectral imaging system, data can be acquired across 100 or more spectral channels, offering superior spectral resolution. However, despite the inherent theoretical advantages of hyperspectral imaging in remote sensing applications, it has proven difficult to extract all of the useful information from these systems because of the overwhelming volume of data generated, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. To address the challenges involved in the analysis of remotely sensed hyperspectral image data, spectral unmixing algorithms have been developed to deconvolve the endmembers and corresponding abundances of components contained within a given image data set. The term endmember refers to the spectral signature of a given pure component. See N. Keshava and J. F. Mustard, “Spectral Unmixing,” IEEE Signal Processing Magazine 19(1), 44 (2002).
One can decompose the traditional spectral unmixing problem into a sequence of two consecutive steps: (1) endmember determination—estimate the set of unique endmembers for the components that comprise the mixed pixels in the scene, and (2) inversion—estimate the relative abundances of the components for each mixed pixel. Endmember determination is typically achieved through the use of laboratory-based library spectra, or the identification of pure pixels in the scene employing a method such as the pixel purity index. See S. J. Young, “Detection and Quantification of Gases in Industrial-Stack Plumes Using Thermal-Infrared Hyperspectral Imaging,” Aerospace Report No. ATR-2002(8407)-1, pp. 1-19, The Aerospace Corporation, El Segundo, Calif., (2002); C. C. Funk et al., “Clustering to Improve Matched Filter Detection of Weak Gas Plumes in Hyperspectral Thermal Imagery,” IEEE Transactions on Geoscience and Remote Sensing 39(7), 1410 (2001); and J. W. Boardman et al., “Mapping target signatures via partial unmixing of AVIRIS data,” Summaries, 5th JPL Airborne Earth Science Workshop, JPL Publication 95-1, 23 (1995). Spectral libraries can be used to decompose individual pixels into their components based on a knowledge and comparison with the spectral characteristics of known target endmembers. However, spectral libraries are limited in that laboratory spectra are not representative of remotely acquired spectra because of differences in instrumentation and acquisition conditions. The use of spectral libraries also assumes an a priori model for the hyperspectral data and this approach fails when the pure spectrum for a component of interest is not contained within the library. While pure pixel-based approaches can be used to estimate endmembers directly from the scene, such approaches are typically time and computation intensive. Furthermore, it may be difficult or impossible to identify pure pixels in remote scenes with limited spatial resolution.
Therefore, a need remains for a method to perform rapid and comprehensive data exploitation of remotely sensed spectral data with limited a priori knowledge regarding the scene. According to the present invention, a fast and rigorous multivariate curve resolution (MCR) algorithm can be used to analyze remotely sensed spectral data. MCR is an algorithmic approach that focuses on recovering the endmember and abundance profiles of the components in an unresolved mixture when little or no prior information is available about the nature and composition of these mixtures.